class_docstr: An implementation of `"NEU" <https://www.ijcai.org/Proceedings/2017/0544.pdf>`_
  from the IJCAI 17 paper "Fast Network Embedding Enhancement via High Order Proximity Approximation".
  The procedure uses an arbitrary embedding and augments it by higher order proximities with a recursive
  meta learning algorithm.
  
  Args:
      L1 (float): Weight of lower order proximities. Defauls is 0.5
      L2 (float): Weight of higher order proximities. Default is 0.25.
      T (int): Number of iterations. Default is 1.
      seed (int): Random seed value. Default is 42.
docstr: Fitting a model and performing NEU.
  
  Args:
      * **graph** *(NetworkX graph)* - The graph to be embedded.
      * **model** *(KC embedding model)* - Karate Club embedding.
docstr_tok: ["Fitting","a","model","and","performing","NEU",".","Args",":","*","*","*","graph","*","*","*","(","NetworkX","graph",")","*","-","The","graph","to","be","embedded",".","*","*","*","model","*","*","*","(","KC","embedding","model",")","*","-","Karate","Club","embedding","."]
code: def fit(self, graph: nx.classes.graph.Graph, model: Estimator):
      r"""
      Fitting a model and performing NEU.
  
      Args:
          * **graph** *(NetworkX graph)* - The graph to be embedded.
          * **model** *(KC embedding model)* - Karate Club embedding.
      """
      self._set_seed()
      graph = self._check_graph(graph)
      self.model = model
      self.model.fit(graph)
      original_embedding = self.model.get_embedding()
      self._embedding = self._update_embedding(
          graph, original_embedding
      )
  
file_path: karateclub/karateclub/node_embedding/meta/neu.py
imports: import numpy
  import networkx
  from sklearn.preprocessing import normalize
  from karateclub.estimator import Estimator
label_desc: Use image node_id 4 for calling the NEU obj's underlying member method code with example usage: obj.fit(graph, model) without return types